The Jobs at Risk Index (JARI)

March 24 2020

Lukas Kikuchi & Ishan Khurana

(Autonomy Data Unit)

Which occupations expose workers to COVID-19 most?

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The COVID-19 virus is spreading across the UK population through close human contact (e.g. via breath and touching shared surfaces). Those workers with jobs that bring them into close contact with others and/or those who regularly come into contact with diseases or infections are most at risk.

In the interactive charts on this page we have plotted 273 different UK-based occupations according to numbers employed in each, the level of physical proximity that each job requires and the exposure to diseases or infections that each job entails.

Each blue orb represents an occupation: if you hover over each you will see a readout of information, including how many men and women do this job and what the median pay is. The larger the orb, the more people employed in that occupation. You’ll find that each job also has a score that ranks it for exposure and for physical proximity. Sales Assistants, for example, have a high proximity to others (scoring 69), but a low exposure – usually – to disease or infections (scoring 19). Nurses score high on both fronts: they involve high physical proximity (94) and a high exposure to diseases and infections (95).

We have then used this data to create a Risk Indication Factor (RIF) that can usefully scope the risk of COVID-19 infection for workers in those occupations. Below we have unpacked this data into different charts so as to highlight key findings. Although the results are only indicative, they reveal much about the UK labour market and the workers who will be most affected by the COVID-19 crisis.

You can arrange the charts in a number of ways, plotting the occupations against different metrics by toggling the corresponding buttons. If you search for a job title the corresponding orb will become highlighted in the chart so you can find it more easily. We are using occupation titles as given by the ONS’ SOC system.

Our study is similar to that which The New York Times published recently, although we have focused on the UK context and have integrated different data to reveal new information.

To make the plot we used the O*NET database that has detailed information on thousands of occupations in the U.S. In their study that utilises surveys, they deployed a scoring system as to both the extent of exposure to disease and infection that each job has, as well as how much physical proximity to others that these jobs entail. They provide:

‘To what extent does this job require the worker to perform job tasks in close physical proximity to other people?’

0 – I don’t work near other people (beyond 100 ft.)

25 – I work with others but not closely (e.g. private office)

50 – Slightly close (e.g. shared office)

75 – Moderately close (at arm’s length)

100 – Very close (near touching)

Responses for each occupation include values between, for instance, 0 and 25 or 75 and 100. E.g. Proximity = 83, Exposure = 63.

We matched the U.S. occupations in O*NET to the ONS Standard Occupational Classification system, allowing us to understand what these metrics mean for UK-based jobs. We could then further break this information down using ONS employment data to reveal the numbers of men and women estimated to be working in each occupation, as well as median pay.

+ 22 of the 28 occupations with the highest risk factor can be classified as 'key workers'

There are 28 occupations that represent ‘High Risk’ jobs and 22 of these roles can be categorised as ‘Key Worker’ occupations. This means that many of these workers will still have to be working even in a ‘lockdown’ situation. These workers are indispensable at the moment and are at the same time those most vulnerable to COVID-19.

+ Nearly 11 million people are in occupations that can be categorised as being at meaningful risk of exposure to COVID-19

Nearly 11 million people are in occupations that have a RIF of above 50: occupations where they would be at Risk.

Over 5 million people are in occupations that have a RIF of above 60: occupations where they would be at Significant Risk.

+ There are 9 million workers in occupations that involve high levels of physical proximity with other people

There are 5 million workers in occupations that have a very high level of physical proximity.

+ 77% of the 'High Risk' workforce are women

Out of over 3,200,000 workers in ‘High Risk’ roles, around 2,500,000 are women.

+ Health care workers are most exposed out of all occupation groups

As the below charts demonstrate, health care workers are at the greatest risk – and many will have to be working despite the government’s measures and suggestions. These workers are indispensable at the moment and are at the same those most vulnerable to COVID-19.

+ The average pay for workers in High Risk occupations is below the median weekly UK wage

The average full-time pay across High Risk occupations is £574 per week. (The median weekly earnings for full-time employees reached £585 in April 2019)

+ Over 1 million High Risk jobs pay poverty wages

The average full-time pay for High Risk jobs paying poverty wages is just £355 per week.

+ 98% of workers in High Risk jobs that are being paid poverty wages are women

Women make up 98% of employment in High Risk, poverty pay roles. By contrast only 2% of High Risk, poverty pay roles are carried out by men.

Employment and exposure to diseases and infections

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In the above interactive chart we have plotted employment numbers against the day to day exposure to diseases or infections that different occupations entail.

Employment and physical proximity to others

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In the above interactive chart we have plotted employment numbers against the physical proximity to other people that different occupations entail. This metric is particularly important in the context of the outbreak and spread of COVID-19 as the virus spreads quickly via close human interaction (breath condensation and touching shared surfaces).

There are 9 million workers in occupations that have a physical proximity score of 70 or above.

There are 5 million workers in occupations that have a physical proximity score of 80 or above.

Employment and overall Risk Indication Factor (RIF)

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In the interactive chart above we have plotted the same occupations against an overall Risk Indication Factor (RIF) that we have produced by combining both variables: physical proximity and exposure to disease and infection. The Risk Indication Factor (RIF) characterises the risk to a job during an outbreak of a communicable disease. This primarily indexes a risk of infection but can also be a useful tool to examine economic risk by identifying occupations that require physical proximity with customers/clients and which have now been affected by social distancing practices.

We define RIF by assuming that both a proximity to others during work and exposure to disease can independently contribute to the job RIF. We therefore aggregate using the equation below in which E is exposure and P is proximity:

The red and green lines represent how far along a job is on the proximity and exposure axes respectively. The Risk Indication is the length of the orange line i.e. the distance from zero proximity and zero exposure. To arrive at the RIF we calculate this distance for a given job using the Pythagorean Theorem and divide this distance by the maximum possible distance (for a job with 100 proximity and 100 exposure). This gives us a RIF value between 0 and 100.

Nursing, for instance, involves intense close physical proximity as well as a high exposure to diseases and infections, and so scores highly in the above chart according to the Risk Indication Factor (95).

‘Care worker and home carer’ is an occupation with a particularly high of employment figure (777,000), high exposure to diseases (63) and high physical proximity to others (84). This group comes out with a RIF of 74.

The average Risk Indication Factor for all occupations is 50.

Incomes, jobs and risk

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In the interactive chart above we have plotted median average pay for each occupation against our Risk Indication Factor. We used a Theil Sen Regression model, from which we infer a correlation between average weekly pay and RIF.

Simply put, moving down pay brackets leads to a higher RIF: the less you get paid, the more at risk you are, generally speaking.

Although this correlation remains strong across both male and female-dominated occupations, the picture changes notably depending on which gender you toggle. This is because median average pay for full-time male workers is markedly higher than it is for female full-time workers in the same roles. It is also because many High Risk roles are carried out predominantly by women.

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We can scope in further and investigate the ‘High Risk’ jobs.

We’re categorising occupations with a RIF of 50 and above as ‘At Risk’ jobs, and occupations with a RIF of 60 and above as ‘Significant Risk’ jobs.

The chart above shows all occupations with a Risk Indication Factor of 70 or above, which we categorise as ‘High Risk ‘Jobs. There are 28 such occupations, covering over 3 million people.

10 million people are in occupations that have a RIF of above 50: occupations where they would be at 'Risk'.

5 million people are in occupations that have a RIF of above 60: occupations where we could say they are at 'Significant Risk'

The purple line on the chart represents the median full-time wage (£585 per week) for full-time workers in the UK. The red line represents the UK poverty line, defined as 2/3 of the median wage (£390 per week). This helps gives us a picture of the income disparities that accompany those occupations that are High Risk.

The average weekly pay within High Risk jobs is £574. We arrive at this value by calculating the weighted mean of the median weekly incomes of full-time High Risk workers.

If we exclude doctors in this analysis – as their income is a clear outlier for High Risk workers – the median weekly pay for full-time High Risk workers is £552.

There are 1,060,400 people in High Risk jobs getting paid poverty wages. The average weekly wage for these High Risk jobs that pay below the poverty line is £355.

Care workers and home carers sit just on the poverty line – with a median average weekly wage of £391. This number obscures gender inequality however, as the median average weekly wage for women is below the poverty line at £384, whilst the median weekly pay for men is £408.

+ Nursery Nurses and Assistants

+ Animal Care Services Occupations

+ Dental Nurses

+ Care Escorts

+ Pharmacy and other Dispensing Assistants

+ (Women) Care Workers and Home Carers

Gender, risk and incomes

The extent of these risks are highly gendered. In the above chart, female-dominated occupations (with workforces of at least 70% women) are represented with orange markers.

Risk is distributed unequally between men and women. The average Risk Indication Factor for female-dominated occupations is 63. By contrast, the average Risk Indication Factor for all male-dominated occupations is 43.

Out of 3,243,400 workers in ‘High Risk’ roles, around 2,522,900 (77%) are women.

Out of the 1,060,400 workers who are in High Risk roles and being paid poverty wages, 1,046,400 are women (98%). The majority of these women workers are in caring jobs.

To find this number: we select the jobs where the median, average weekly pay for women is below the poverty line for wages. We then remove all the jobs with a RIF below 70 which leaves us with the High Risk jobs. We then count the number of women in these jobs. This gives us 1,046,400. Similarly for men: We select the jobs where the median, average weekly pay for men is below the poverty line for wages. We then remove all the jobs with a RIF below 70 which leaves us with the High Risk jobs. We then count the number of men in these jobs. This gives us 14,000 (according to ASHE figures).

The wage range for full-time High-Risk jobs with overwhelmingly female workforces is wide. It spans from £345 per week (for Pharmacy and other Dispensing Assistants) and £804 per week (for Pharmacists).

Note: ONS data on median pay per occupation has certain margins of error (for most occupations, this margin is in the main between 0 and 10%). We’ve used the median pay for each occupation for approximately 97% of occupations, and in 3% of cases we’ve used the mean.